AI-Based Mobile Application for Biomechanical Assessment and Visualization of Infant Carrying Posture

Abstract

Postpartum mothers are especially vulnerable to musculoskeletal disorders due to the physical demands of childcare. Inappropriate infant carrying posture frequently leads to wrist tenosynovitis, lower back pain, or pelvic strain. If unaddressed, these problems may become chronic, hinder daily caregiving, and reduce overall maternal well-being. Early intervention is therefore essential. However, until now, mothers have lacked a simple and objective tool to identify problems in their own carrying posture and to receive timely guidance.To address this gap, we developed a mobile application that provides AI-based biomechanical assessment and visualization of infant carrying posture. The application was designed with simplicity and usability in mind, requiring only a few taps to capture an image and generate immediate feedback. Five evidence-based ergonomic indicators form the evaluation framework: (1) carrying height relative to the torso, (2) closeness of caregiver–infant body contact, (3) degree of arm abduction, (4) shoulder balance, and (5) spinal alignment. Unlike conventional assessments that rely on expert observation or specialized equipment, this system offers visual feedback that compares the user’s posture with an ideal model, enabling mothers to recognize specific differences and take corrective action without requiring professional expertise.Validation was conducted by comparing system output with expert evaluations. Results demonstrated moderate to substantial agreement: Kendall’s posture (Accuracy 0.667, κ = 0.333), Infant closeness (Accuracy 0.750, κ = 0.500), Infant’s vertical position (Accuracy 0.821, κ = 0.643), and Armpit openness (Accuracy 0.857, κ = 0.714), with a macro-average of Accuracy 0.774 and κ = 0.548. These results indicate that the application reliably approximates expert judgment across multiple aspects of infant carrying posture.Equally important, user testing highlighted the impact of the design approach. Mothers reported that the app was intuitive and efficient, with assessments completed in seconds. The visualization of differences between actual and ideal posture was rated as highly useful for self-correction and for raising awareness of ergonomic carrying habits. Participants also noted the potential value of using the app jointly with professionals such as midwives and public health nurses, enhancing consultations with objective posture data and visual explanations.This study contributes to mobile health and ergonomics by translating clinical knowledge into an accessible everyday tool. It demonstrates how AI-driven posture recognition can support early intervention, helping postpartum mothers prevent musculoskeletal problems while ensuring infant safety. By combining biomechanical assessment with intuitive visualization, the application empowers caregivers with actionable feedback for healthier childcare practices.

Keywords: infant carrying posture, AI-based assessment, biomechanics, visualization, mobile health application

DOI: 10.54941/ahfe1006973